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Krishnamurthy M, You X, Sepeta LN, Matuska E, Oluigbo C, Berl MM, Gaillard WD, Gholipour T. Resting-state functional MRI for motor cortex mapping in childhood-onset focal epilepsy. J Neuroimaging 2022; 32:1201-1210. [PMID: 35881496 PMCID: PMC9649846 DOI: 10.1111/jon.13030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/08/2022] [Accepted: 07/12/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Task-based functional MRI (fMRI) mapping of the motor function prior to epilepsy surgery has limitations in children with epilepsy. We present a data-driven method to automatically delineate the motor cortex using task-free, resting-state fMRI (rsfMRI) data. METHODS We used whole-brain rsfMRI for independent component analysis (ICA). A template matching process with Discriminability Index-based Component Identification score was used for each participant to select and combine motor ICA components in their native brain space, resulting in a whole-brain ICA Motor Map (wIMM). We validated wIMM by comparing individual results with bilateral finger-tapping motor task fMRI activation, and evaluated its reproducibility in controls. RESULTS Data from 64 patients and 12 controls were used to generate group wIMM maps. The hit rate between wIMM and motor task activation ranged from 60% to 79% across all participants. Sensitivity of wIMM for capturing the task activation peak was 87.5% among 32 patients and 100% in 12 controls with available motor task results. We also showed high similarity in repeated runs in controls. CONCLUSIONS Our results show the sensitivity and reproducibility of an automated motor mapping method based on ICA analysis of rsfMRI in children with epilepsy. The ICA maps may provide different, but useful, information than task fMRI. Future studies will expand our method to mapping other brain functions, and may lead to a surgical planning tool for patients who cannot perform task fMRI and help predict their postsurgical function.
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Affiliation(s)
- Manu Krishnamurthy
- Center for Neuroscience, Children’s National Hospital, the George Washington University, Washington, DC
| | - Xiaozhen You
- Center for Neuroscience, Children’s National Hospital, the George Washington University, Washington, DC
| | - Leigh N. Sepeta
- Center for Neuroscience, Children’s National Hospital, the George Washington University, Washington, DC
| | - Emily Matuska
- Center for Neuroscience, Children’s National Hospital, the George Washington University, Washington, DC
| | - Chima Oluigbo
- Center for Neuroscience, Children’s National Hospital, the George Washington University, Washington, DC
| | - Madison M. Berl
- Center for Neuroscience, Children’s National Hospital, the George Washington University, Washington, DC
| | - William D. Gaillard
- Center for Neuroscience, Children’s National Hospital, the George Washington University, Washington, DC
| | - Taha Gholipour
- Center for Neuroscience, Children’s National Hospital, the George Washington University, Washington, DC
- Department of Neurology, the George Washington University, Washington, DC
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2
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Yoo K, Rosenberg MD, Kwon YH, Scheinost D, Constable RT, Chun MM. A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome. Neuroimage 2022; 257:119279. [PMID: 35577026 PMCID: PMC9307138 DOI: 10.1016/j.neuroimage.2022.119279] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 04/11/2022] [Accepted: 05/02/2022] [Indexed: 11/07/2022] Open
Abstract
The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model context-dependent changes. Here, we present a novel connectome-to-connectome (C2C) transformation framework that enables us to model the brain's functional reorganization from one connectome state to another in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-related connectomes from their task-free (resting-state) connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions with increased power, achieving similar performance with just a third of the subjects needed when relying on resting-state data alone. Finally, the C2C model reveals how the brain reorganizes between cognitive states. Our observations support the existence of reliable state-specific subsystems in the brain and demonstrate that we can quantitatively model how the connectome reconfigures to different cognitive states, enabling more accurate predictions of behavior with fewer subjects.
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Affiliation(s)
- Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America.
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America,Department of Psychology, University of Chicago, Chicago, IL 60637, United States of America
| | - Young Hye Kwon
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, United States of America
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, United States of America,Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, United States of America,Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06510, United States of America
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America,Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, United States of America,Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, United States of America
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Wang J, Khosrowabadi R, Ng KK, Hong Z, Chong JSX, Wang Y, Chen CY, Hilal S, Venketasubramanian N, Wong TY, Chen CLH, Ikram MK, Zhou J. Alterations in Brain Network Topology and Structural-Functional Connectome Coupling Relate to Cognitive Impairment. Front Aging Neurosci 2018; 10:404. [PMID: 30618711 PMCID: PMC6300727 DOI: 10.3389/fnagi.2018.00404] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 11/23/2018] [Indexed: 12/13/2022] Open
Abstract
According to the network-based neurodegeneration hypothesis, neurodegenerative diseases target specific large-scale neural networks, such as the default mode network, and may propagate along the structural and functional connections within and between these brain networks. Cognitive impairment no dementia (CIND) represents an early prodromal stage but few studies have examined brain topological changes within and between brain structural and functional networks. To this end, we studied the structural networks [diffusion magnetic resonance imaging (MRI)] and functional networks (task-free functional MRI) in CIND (61 mild, 56 moderate) and healthy older adults (97 controls). Structurally, compared with controls, moderate CIND had lower global efficiency, and lower nodal centrality and nodal efficiency in the thalamus, somatomotor network, and higher-order cognitive networks. Mild CIND only had higher nodal degree centrality in dorsal parietal regions. Functional differences were more subtle, with both CIND groups showing lower nodal centrality and efficiency in temporal and somatomotor regions. Importantly, CIND generally had higher structural-functional connectome correlation than controls. The higher structural-functional topological similarity was undesirable as higher correlation was associated with poorer verbal memory, executive function, and visuoconstruction. Our findings highlighted the distinct and progressive changes in brain structural-functional networks at the prodromal stage of neurodegenerative diseases.
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Affiliation(s)
- Juan Wang
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Reza Khosrowabadi
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore.,Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Kwun Kei Ng
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Zhaoping Hong
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Joanna Su Xian Chong
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Yijun Wang
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Chun-Yin Chen
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Saima Hilal
- Department of Pharmacology, National University of Singapore, Singapore, Singapore
| | | | - Tien Yin Wong
- Memory Aging & Cognition Centre, National University Health System, Singapore, Singapore.,Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | | | - Mohammad Kamran Ikram
- Department of Pharmacology, National University of Singapore, Singapore, Singapore.,Memory Aging & Cognition Centre, National University Health System, Singapore, Singapore.,Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore.,Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Juan Zhou
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore.,Clinical Imaging Research Centre, The Agency for Science, Technology and Research-National University of Singapore, Singapore, Singapore
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5
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Marchitelli R, Minati L, Marizzoni M, Bosch B, Bartrés-Faz D, Müller BW, Wiltfang J, Fiedler U, Roccatagliata L, Picco A, Nobili F, Blin O, Bombois S, Lopes R, Bordet R, Sein J, Ranjeva JP, Didic M, Gros-Dagnac H, Payoux P, Zoccatelli G, Alessandrini F, Beltramello A, Bargalló N, Ferretti A, Caulo M, Aiello M, Cavaliere C, Soricelli A, Parnetti L, Tarducci R, Floridi P, Tsolaki M, Constantinidis M, Drevelegas A, Rossini PM, Marra C, Schönknecht P, Hensch T, Hoffmann KT, Kuijer JP, Visser PJ, Barkhof F, Frisoni GB, Jovicich J. Test-retest reliability of the default mode network in a multi-centric fMRI study of healthy elderly: Effects of data-driven physiological noise correction techniques. Hum Brain Mapp 2016; 37:2114-32. [PMID: 26990928 DOI: 10.1002/hbm.23157] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 02/16/2016] [Accepted: 02/17/2016] [Indexed: 12/31/2022] Open
Abstract
Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within-site test-retest reliability and the across-site reproducibility consistency of DMN-derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue-based regression, PESTICA and FSL-FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z-scores and, albeit less markedly, the cluster-size in the DMN; in particular, FSL-FIX tended to increase the DMN z-scores compared to others. Within-site test-retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5-11% for DMN z-scores and cluster-size reliability. DMN pattern overlap was in the range 60-65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL-FIX and Tissue-based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z-scores relative to NPC. Overall these findings support the use of rPNC methods like tissue-based or FSL-FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114-2132, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Rocco Marchitelli
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
| | - Ludovico Minati
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy.,Scientific Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Moira Marizzoni
- LENITEM Laboratory of Epidemiology, Neuroimaging, & Telemedicine-IRCCS San Giovanni Di Dio-FBF, Brescia, Italy
| | - Beatriz Bosch
- Alzheimer's Disease and Other Cognitive Disorders Unit, Department of Neurology, Hospital Clínic, and IDIBAPS, Barcelona, Spain
| | - David Bartrés-Faz
- Department of Psychiatry and Clinical Psychobiology, Universitat De Barcelona and IDIBAPS, Barcelona, Spain
| | - Bernhard W Müller
- LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany
| | - Jens Wiltfang
- LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center (UMG), Georg August University, Göttingen, Germany
| | - Ute Fiedler
- LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany
| | - Luca Roccatagliata
- Department of Neuroradiology, IRCSS San Martino University Hospital and IST, Genoa, Italy.,Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Agnese Picco
- Department of Neuroscience, Ophthalmology, Genetics and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Flavio Nobili
- Department of Neuroscience, Ophthalmology, Genetics and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Oliver Blin
- Pharmacology, Assistance Publique - Hôpitaux De Marseille, Aix-Marseille University-CNRS, UMR, Marseille, 7289, France
| | - Stephanie Bombois
- University of Lille, INSERM, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
| | - Renaud Lopes
- University of Lille, INSERM, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
| | - Régis Bordet
- University of Lille, INSERM, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
| | - Julien Sein
- CRMBM-CEMEREM, UMR 7339, Aix Marseille Université-CNRS, Marseille, France
| | | | - Mira Didic
- APHM, CHU Timone, Service De Neurologie Et Neuropsychologie, Marseille, France.,Aix-Marseille Université, INSERM INS UMR_S 1106, Marseille, 13005, France
| | - Hélène Gros-Dagnac
- INSERM, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, Toulouse, France.,Université De Toulouse, UPS, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, CHU Purpan, Place Du Dr Baylac, Toulouse Cedex 9, France
| | - Pierre Payoux
- INSERM, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, Toulouse, France.,Université De Toulouse, UPS, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, CHU Purpan, Place Du Dr Baylac, Toulouse Cedex 9, France
| | | | | | | | - Núria Bargalló
- Department of Neuroradiology and Magnetic Resonace Image Core Facility, Hospital Clínic De Barcelona, IDIBAPS, Barcelona, Spain
| | - Antonio Ferretti
- Department of Neuroscience Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti, Italy.,Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio" of Chieti, Italy
| | - Massimo Caulo
- Department of Neuroscience Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti, Italy.,Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio" of Chieti, Italy
| | | | | | - Andrea Soricelli
- IRCCS SDN, Naples, Italy.,University of Naples Parthenope, Naples, Italy
| | - Lucilla Parnetti
- Section of Neurology, Centre for Memory Disturbances, University of Perugia, Perugia, Italy
| | | | - Piero Floridi
- Perugia General Hospital, Neuroradiology Unit, Perugia, Italy
| | - Magda Tsolaki
- 3rd Department of Neurology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Antonios Drevelegas
- Interbalkan Medical Center of Thessaloniki, Thessaloniki, Greece.,Department of Radiology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Paolo Maria Rossini
- Department of Geriatrics, Neuroscience & Orthopaedics, Catholic University, Policlinic Gemelli, Rome, Italy.,IRCSS S.Raffaele Pisana, Rome, Italy
| | - Camillo Marra
- Center for Neuropsychological Research, Catholic University, Rome, Italy
| | - Peter Schönknecht
- Department of Psychiatry, University Hospital Leipzig, Leipzig, Germany
| | - Tilman Hensch
- Department of Psychiatry, University Hospital Leipzig, Leipzig, Germany
| | | | - Joost P Kuijer
- Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, the Netherlands
| | - Pieter Jelle Visser
- Alzheimer Centre and Department of Neurology, Vrije Universiteit University Medical Center, Amsterdam, the Netherlands.,Department of Psychiatry and Neuropsychology, Alzheimer Center Limburg, University of Maastricht, Maastricht, the Netherlands
| | - Frederik Barkhof
- Alzheimer Centre and Department of Neurology, Vrije Universiteit University Medical Center, Amsterdam, the Netherlands
| | - Giovanni B Frisoni
- LENITEM Laboratory of Epidemiology, Neuroimaging, & Telemedicine-IRCCS San Giovanni Di Dio-FBF, Brescia, Italy.,Memory Clinic and LANVIE, Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
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